Gunther Cox Salvius the Robot

Springtime arm mockup

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In addition to a pair of new hands for the robot, I am also experimenting with a new design for the arm. At the moment I have created a mock-up in cardboard so that I can make changes easily. The new arm adds shoulder and wrist joints that were not previously included in the design.

Many thanks to +Western New England University for donating the pens which will be used to make two new hands for the robot. For anyone interested in how the pens will be used checkout my original post here. The driving force behind the design of this robot is to design a way for humanoid robots to be constructed with minimal costs by taking advantage of materials that are radially available, and also recycling to help reduce costs.

Happy Earth day everyone and thank you for your continued support!

Redesigned rotary movement for shoulder

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I've moved the motor that rotates the robot's arm inside of the chest cavity to save space. The previous arm configuration was based off of one of my first tests for mounting the robot's arms which used a motor mounted in the arm. Mounting the motor inside of the robot's chest cavity will allow me to reduce the weight of the robot's arms for future versions.

I'm currently modeling a new left arm for the robot which will incorporate more joints for an improved range of motion.

Arc reactor installation complete

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The photo above shows the robot's arc reactor which, as of April 1st, 2014 has been fully installed. This upgrade will provide enough electrical output to fuel the robot's tremendous power requirements. Currently I'm using a small sample of palladium to catalyst the fission reaction in the arc reactor, however it seems that the reactor is accumulating excessive quantities of neutrinos which have been building up on the surface of the laser emitter array. Once I determine an efficient way to keep neutrinos out of the arc reaction I have estimated that the final output should yield exactly 1.21 gigawatts of clean usable energy.

JPG vs BMP vs PNG vs GIF

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This was a quick experiment I did to check the efficiency of a few image formats. For this experiment I base64 encoded a 1 pixel image file of each of the following image file formats: .jpg, .bmp, .png, .gif.

Here is the base64 encoded results of each image:

.jpg - From a 539 byte file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.bmp - From a 126 byte file
Qk1+AAAAAAAAAHoAAABsAAAAAQAAAAEAAAABABgAAAAAAAQAAAATCwAAEwsAAAAAA
AAAAAAAQkdScwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAIAAAAAAAAAAAAAAAAAAAD///8A

.png - From a 69 byte file
iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAADElEQVQI12P4//8/AAX+Av
7czFnnAAAAAElFTkSuQmCC

.gif - From a 35 byte file
R0lGODdhAQABAIAAAP///////ywAAAAAAQABAAACAkQBADs=

Based on these results it is clear that a 1 pixel gif image is the most efficient format for base64 encoding data.

Update:
I conducted a further study of the efficiency of these image formats by generating a series of images of each type ranging from 1 pixel to 2000 pixels and skipping by increments of 200. To make the testing go quicker, I created a script in Python that generates a set of images and logs their file size and dimensions. My script also generates a graph based on these logs to help visualize the data.
If your interested seeing the code or generating the logs for yourself you can download the script from https://gist.github.com/gunthercox/9874531.

For this graph, the horizontal axis is the physical dimensions of the file, and the vertical axis is the file size.
.jpeg = red
.bmp = black
.png = green
.gif = blue

The data from the logs shows that the png to be on average the most efficient format. Jpegs and gifs have very close performance for smaller images but jpegs quickly increase in size for larger images. Bitmap images (.bmp) were surprisingly inefficient and while all these formats had very close sizes for a 1 pixel image file, the bmp image grew tremendously larger as the size of the file was increased.

USB Pinouts

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Additional note: As of Battery Charging Specification 1.2 which was released in December of 2010, USB ports can provide current to charging devices drawing 1.5 A with a maximum current of 5 A.

Salvius's Brain

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Salvius's brain is a network of microprocessors which receive and carry out various functions based on input from the robot's API. In other words, what follows will be some insight into the logic behind this collection of components housed in Altoids tins and held together with rubber bands. Salvius uses several controllers connected through I2C (two wire connections). An important feature of this network is that work is distributed over multiple processors, this provides many advantages. For example, if one of the controllers fails, it does not greatly affect any of the controllers. A hardware failure may cause the robot may loose the ability to speak or receive input, but the likeliness of multiple simultaneous errors is very low. The modular nature of this setup also makes it easy to add and remove controllers to the network.

A central nervous system

Similar to the robot's brain, a network of I2C compatible motor controllers receives commands from the brain and responds by carrying out a corresponding action. This is a lot like the central nervous system seen in animals which has some processing power of its own. At the moment, Salvius's central nervous system is limited to the movement of the head and arms, however work is in progress to extend motor control to the robot's legs.

Security with Humanoid Robots

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The issue of security has become a hot topic when looking at the future of robotics. The present concern is that robots who could learn to interact with humans would inevitably need to store data about the daily lives of those individuals that the robot knows. A robot designed to assist people would need to be able to learn to navigate through their house and it would need to know where different items are kept. A robot like this would be perfect for someone with a physical disability, however it makes them vulnerable to a variety of new cyber crimes. Proper encryption could hep to protect owner's personal information but there is still risks. Gaining access to the information stored by one of these hypothetical robots could be accomplished with no greater difficulty than with which other cyber-crimes are committed. At the moment there little cause for concern. as Nicolas Rigaud of Aldebaran Robotics makes clear "[The market for robots in consumer households] doesn't exist yet".

Another issue that I want to bring up is with copyrights. What happens in the event that a robot reads a book to someone, or sees a photo. Basically the robot would be storing copyrighted content. We have no way for robots to determine what content is something important to remember, and what is intellectual property. However, this may be one area where this will end up being allowed, since there is no threat of a robot filing a patent or trying to make money off of someone else's intellectual property.

Thoughts?